CI/CD for Pipelines

Concepts covered: paCiCd, paSlimCi

Application engineers ship changes through CI/CD: a pull request runs unit tests, integration tests, and a deploy step. Pipeline changes are different in two ways. First, the data is part of the test surface; a transform is correct only if it produces the right output on real-shaped input. Second, the pipeline operates on data the test environment may not have. Both differences shape what CI for pipelines looks like, and both are misunderstood by teams that try to import application CI patterns wholesale. The early signal that a CI strategy is mismatched is the engineers' habit of running pipelines locally before pushing, even when CI exists. They are not lazy or untrusting; they are working around a CI shape that does not catch what production is going to catch. The fix is not to demand t

About This Interactive Section

This section is part of the Pipeline Operations: Intermediate lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

How DataDriven Lessons Work

DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.